Maturity determination of fresh okra fruit is a crucial issue for farmers to optimize harvest date for good taste and economic return. In this study, visible and near infrared (Vis-NIR) hyperspectral imaging was employed to evaluate the maturity stage and moisture content of fresh okra fruit precisely. Immature, mature and post-mature okra samples were identified by measuring the shear force, and physicochemical analysis indicated the negative correlation between maturity and moisture content. A library for support vector machines (LIBSVM) model was developed using effective wavelengths, texture features and their fusion, respectively. The LIBSVM model using the fused dataset obtained the highest total maturity classification accuracy reaching 91.7 % for cross-validation. Further investigation was conducted to predict moisture content in okra with different maturity by multiple linear regression (MLR) model, and the determination coefficient, the root mean square error and residual predictive deviation of cross-validation was $R_{cv}^2$ = 0.816, RMSECV = 1.348 %, ${RPD}_{cv}$ = 2.333, respectively. After that spatial distribution map was generated to visualize the moisture content in okra fruit. These results demonstrated the potential of hyperspectral imaging for maturity determination and moisture content changes during growth, providing the support for the development of field monitoring sensor to determine the optimum harvest date of okra fruit.

新鲜秋葵果实的成熟度测定是农民优化收成日期以获得良好口味和经济回报的关键问题。在这项研究中，使用可见和近红外（Vis-NIR）高光谱成像来精确评估新鲜秋葵果实的成熟阶段和水分含量。通过测量剪切力来鉴定未成熟，成熟和成熟的秋葵样品，理化分析表明成熟度和水分含量之间呈负相关。使用有效波长，纹理特征及其融合分别开发了支持向量机（LIBSVM）模型库。使用融合数据集的LIBSVM模型获得最高的总成熟度分类精度，达到交叉验证的91.7％。${\mathrm{[R}}_{Cv}^{\mathrm{2个}}$ = 0.816，RMSECV = 1.348％， ${\mathrm{[R}Pd}_{Cv}$分别为2.333。之后，生成了空间分布图以可视化秋葵果实中的水分含量。这些结果证明了高光谱成像技术在确定成熟度和生长过程中水分含量方面的潜力，为确定黄秋葵果实最佳收获日期的田间监测传感器的开发提供了支持。